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Efficient Live Public Transport Data Sharing for Route Planning on the Web

  • Julián Andrés RojasEmail author
  • Dylan Van Assche
  • Harm Delva
  • Pieter Colpaert
  • Ruben Verborgh
Conference paper
  • 359 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12128)

Abstract

Web-based information services transformed how we interact with public transport. Discovering alternatives to reach destinations and obtaining live updates about them is necessary to optimize journeys and improve the quality of travellers’ experience. However, keeping travellers updated with opportune information is demanding. Traditional Web APIs for live public transport data follow a polling approach and allocate all data processing on either data providers, lowering data accessibility, or data consumers, increasing the costs of innovative solutions. Moreover, data processing load increases further because previously obtained route plans are fully recalculated when live updates occur. In between solutions sharing processing load between clients and servers, and alternative Web API architectures were not thoroughly investigated yet. We study performance trade-offs of polling and push-based Web architectures to efficiently publish and consume live public transport data. We implement (i) alternative architectures that allow sharing data processing load between clients and servers, and evaluate their performance following polling- and push-based approaches; (ii) a rollback mechanism that extends the Connection Scan Algorithm to avoid unnecessary full route plan recalculations upon live updates. Evaluations show polling as a more efficient alternative on CPU and RAM but hint towards push-based alternatives when bandwidth is a concern. Clients update route plan results 8–10 times faster with our rollback approach. Smarter API design combining polling and push-based Web interfaces for live public transport data reduces the intrinsic costs of data sharing by equitably distributing the processing load between clients and servers. Future work can investigate more complex multimodal transport scenarios.

Keywords

Public transport Web interfaces Live updates Route planning 

References

  1. 1.
    Ably: The maturity of public transport APIs 2019. Technical report (2019). https://files.ably.io/research/whitepapers/the-maturity-of-public-transport-apis-2019-ably-realtime.pdf
  2. 2.
    Agarwal, S.: Toward a push-scalable global internet. In: 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 786–791, April 2011.  https://doi.org/10.1109/INFCOMW.2011.5928918
  3. 3.
    Bast, H., et al.: Route planning in transportation networks. CoRR abs/1504.05140 (2015). http://arxiv.org/abs/1504.05140
  4. 4.
    Bast, H., Hertel, M., Storandt, S.: Scalable transfer patterns. In: 2016 Proceedings of the Eighteenth Workshop on Algorithm Engineering and Experiments (ALENEX) (2016)Google Scholar
  5. 5.
    Brakewood, C., Macfarlane, G.S., Watkins, K.: The impact of real-time information on bus ridership in new york city. Trans. Res. Part C: Emerg. Technol. 53, 59–75 (2015).  https://doi.org/10.1016/j.trc.2015.01.021. http://www.sciencedirect.com/science/article/pii/S0968090X15000297CrossRefGoogle Scholar
  6. 6.
    Cirillo, F., et al.: Atomic services: sustainable ecosystem of smart city services through pan-European collaboration. In: 2019 Global IoT Summit (GIoTS), pp. 1–7, June 2019.  https://doi.org/10.1109/GIOTS.2019.8766431
  7. 7.
    Colpaert, P., Verborgh, R., Mannens, E.: Public transit route planning through lightweight linked data interfaces. In: Cabot, J., De Virgilio, R., Torlone, R. (eds.) ICWE 2017. LNCS, vol. 10360, pp. 403–411. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-60131-1_26CrossRefGoogle Scholar
  8. 8.
    Dell’Aglio, D., Della Valle, E., Calbimonte, J.P., Corcho, O.: RSP-QL semantics: a unifying query model to explain heterogeneity of RDF stream processing systems. Int. J. Seman. Web Inf. Syst. (IJSWIS) 10(4), 17–44 (2014)CrossRefGoogle Scholar
  9. 9.
    Dell’Aglio, D., Le Phuoc, D., Le-Tuan, A., Ali, M.I., Calbimonte, J.P.: On a Web of data streams. In: ISWC 2017 - DeSemWeb (2017)Google Scholar
  10. 10.
    Delling, D., Dibbelt, J., Pajor, T.: Fast and exact public transit routing with restricted pareto sets. In: Proceedings of the Twenty-First Workshop on Algorithm Engineering and Experiments, ALENEX 2019, San Diego, CA, USA, January 7–8, 2019, pp. 54–65 (2019).  https://doi.org/10.1137/1.9781611975499.5
  11. 11.
    Deolasee, P., Katkar, A., Panchbudhe, A., Ramamritham, K., Shenoy, P.: Adaptive push-pull: disseminating dynamic web data. In: Proceedings of the 10th International Conference on World Wide Web, WWW 2001, pp. 265–274. Association for Computing Machinery, Hong Kong, Hong Kong (2001).  https://doi.org/10.1145/371920.372066
  12. 12.
    Dibbelt, J., Pajor, T., Strasser, B., Wagner, D.: Connection scan algorithm. J. Exp. Algorithmics 23, 1.7:1–1.7:56 (2018).  https://doi.org/10.1145/3274661, http://doi.acm.org/10.1145/3274661
  13. 13.
    Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische Mathematik 1, 269–271 (1959).  https://doi.org/10.1007/BF01386390, https://link.springer.com/10.1007/BF01386390
  14. 14.
    Dziekan, K., Kottenhoff, K.: Dynamic at-stop real-time information displays for public transport: effects on customers. Transp. Res. Part A: Policy Pract. 41(6), 489–501 (2007).  https://doi.org/10.1016/j.tra.2006.11.006. http://www.sciencedirect.com/science/article/pii/S0965856406001431CrossRefGoogle Scholar
  15. 15.
    Fayyaz, S.K., Liu, X.C., Zhang, G.: An efficient general transit feed specification (GTFS) enabled algorithm for dynamic transit accessibility analysis. PLoS ONE 12(10), 1–22 (2017). https://doi.org/10.1371/journal.pone.0185333dCrossRefGoogle Scholar
  16. 16.
    Janowicz, K., Haller, A., Cox, S.J., Phuoc, D.L., Lefrançois, M.: SOSA: a lightweight ontology for sensors, observations, samples, and actuators. J. Web Semant. 56, 1–10 (2019).  https://doi.org/10.1016/j.websem.2018.06.003. http://www.sciencedirect.com/science/article/pii/S1570826818300295CrossRefGoogle Scholar
  17. 17.
    Le-Phuoc, D., Dao-Tran, M., Parreira, J.X., Hauswirth, M.: A native and adaptive approach for unified processing of linked streams and Linked Data. In: International Semantic Web Conference, pp. 370–388 (2011)Google Scholar
  18. 18.
    Loreto, S., Saint-Andre, P., Salsano, S., Wilkins, G.: Known issues and best practices for the use of long polling and streaming in bidirectional HTTP. RFC 6202, April 2011. https://tools.ietf.org/html/rfc6202
  19. 19.
    Malviya, N., Madden, S., Bhattacharya, A.: A continuous query system for dynamic route planning. In: 2011 IEEE 27th International Conference on Data Engineering, pp. 792–803, April 2011.  https://doi.org/10.1109/ICDE.2011.5767844
  20. 20.
    Martin-Flatin, J.P.: Push vs. pull in Web-based network management. In: Integrated Network Management VI. Distributed Management for the Networked Millennium. Proceedings of the Sixth IFIP/IEEE International Symposium on Integrated Network Management. (Cat. No.99EX302), pp. 3–18, May 1999.  https://doi.org/10.1109/INM.1999.770671
  21. 21.
    Monzon, A., Hernandez, S., Cascajo, R.: Quality of bus services performance: benefits of real time passenger information systems. Transp. Telecommun. J. 14(2), 155–166 (2013)CrossRefGoogle Scholar
  22. 22.
    Pajor, T.: Algorithm Engineering for Realistic Journey Planning in Transportation Networks. Ph.D. thesis (2013). https://d-nb.info/1058165240/34
  23. 23.
    Pimentel, V., Nickerson, B.G.: Communicating and displaying real-time data with websocket. IEEE Internet Comput. 16(4), 45–53 (2012).  https://doi.org/10.1109/MIC.2012.64CrossRefGoogle Scholar
  24. 24.
    Rojas, J.A., Chaves-Fraga, D., Colpaert, P., Verborgh, R., Mannens, E.: Providing reliable access to real-time and historic public transport data using linked connections. In: Proceedings of the ISWC 2017 Posters & Demonstrations and Industry Tracks (2017). http://ceur-ws.org/Vol-1963/paper637.pdf
  25. 25.
    Rojas, J.A., Van de Vyvere, B., Gevaert, A., Taelman, R., Colpaert, P., Verborgh, R.: A preliminary open data publishing strategy for live data in flanders. In: Companion Proceedings of the The Web Conference 2018. WWW ’18 (2018)Google Scholar
  26. 26.
    Stonebraker, M., Çetintemel, U., Zdonik, S.B.: The 8 requirements of real-time stream processing. SIGMOD Record 34, 42–47 (2005)CrossRefGoogle Scholar
  27. 27.
    Taelman, R., Verborgh, R., Colpaert, P., Mannens, E.: Continuous client-side query evaluation over dynamic linked Data. In: Sack, H., Rizzo, G., Steinmetz, N., Mladenić, D., Auer, S., Lange, C. (eds.) ESWC 2016. LNCS, vol. 9989, pp. 273–289. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-47602-5_44CrossRefGoogle Scholar
  28. 28.
    Tommasini, R., et al.: VoCaLS: vocabulary and catalog of linked streams. In: Vrandečić, D., et al. (eds.) ISWC 2018. LNCS, vol. 11137, pp. 256–272. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-00668-6_16CrossRefGoogle Scholar
  29. 29.
    Verborgh, R., Vander Sande, M., Colpaert, P., Coppens, S., Mannens, E., Van de Walle, R.: Web-scale querying through Linked Data Fragments. In: Bizer, C., Heath, T., Auer, S., Berners-Lee, T. (eds.) Proceedings of the 7th Workshop on Linked Data on the Web. CEUR Workshop Proceedings, vol. 1184, April 2014Google Scholar
  30. 30.
    Witt, S.: Trip-based public transit routing using condensed search trees. In: ATMOS (2016). https://arxiv.org/pdf/1607.01299.pdf

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.IDLab, Department of Electronics and Information SystemsGhent University – imecGhentBelgium

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